A Hybrid Texture Coding Method for Fast Texture Mapping

Space research organizations, hospitals and military air surveillance activities, among others, produce a huge amount of data in the form of images hence a large storage space is required to record this information. In hospitals, data produced during medical examination is in the form of a sequence of images and are very much correlated; because these images have great importance, some kind of lossless image compression technique is needed. Moreover, these images are often required to be transmitted over the network. Since the availability of storage and bandwidth is limited, a compression technique is required to reduce the number of bits to store these images and take less time to transmit them over the network. For this purpose, there are many state-of the-art lossless image compression algorithms like CALIC, LOCO-I, JPEG-LS, JPEG20000; Nevertheless, these compression algorithms take only a single file to compress and cannot exploit the correlation among the sequence frames of MRI or CE images. To exploit the correlation, a new algorithm is proposed in this paper. The primary goals of the proposed compression method are to minimize the memory resource during storage of compressed data as well as minimize the bandwidth requirement during transmission of compressed data. For achieving these goals, the proposed compression method combines the single image compression technique called super spatial structure prediction with inter-frame coding to acquire grater compression ratio. An efficient compression method requires elimination of redundancy of data during compression; therefore, for elimination of redundancy of data, initially, the super spatial structure prediction algorithm is applied with the fast block matching approach and later Huffman coding is applied for reducing the number of bits required for transmitting and storing single pixel value. Also, to speed up the block-matching process during motion estimation, the proposed method compares those blocks that have identical sum and leave the others; therefore, the time taken by the block-matching process is reduced by minimizing the unnecessary overhead during the blockmatching process. Thus, in the proposed fast lossless compression method for medical image sequences, the twostage redundant data elimination process ultimately reduces the memory resource required for storing and transmission. The method is tested on the sequences of MRI and CE images and produces an improved compression rate.

It has been shown that image compression based on principal component analysis (PCA) provides good compression efficiency for hyperspectral images. However, PCA might fail to capture all the discriminant information of hyperspectral images, since features that are important for classification tasks may not be high in signal energy. To deal with this problem, we propose a hybrid compression method for hyperspectral images with pre-encoding discriminant information. A feature extraction method is first applied to the original images, producing a set of feature vectors that are used to generate feature images and then residual images by subtracting the feature-reconstructed images from the original ones. Both feature images and residual images are compressed and transmitted. Experiments on data from the Airborne Visible/Infrared Imaging Spectrometer sensor indicate that the proposed method provides better compression efficiency with improved classification accuracy than conventional compression methods.

Wireless Body Area Networks (WBANs) consists of a number of sensors that are placed in the patient's body. These sensors receive vital signs from the patient's body and send it to the medical center. However, due to the fact that these sensors are installed inside the body, it is not easy to replace and charge their batteries and they are seriously faced with energy limitations. One of the efficient ways to save energy for sensors is to compress data. In this regard, in this paper, a compression method called Ultra Energy Efficient Lossless Compression (UEELC) method is presented. In UEELC less data is actually sent to the sink node, which consumes less energy. This is done by converting each input string to a shorter code in several steps. In the first step, each input string is converted to the ASCII equivalent of that string and then the resulting ASCII code is converted to its binary equivalent. In the next step, by counting the number of zeros and ones in the resulting binary code, a more optimal traverse is selected. And in the final step, more efficient traversing is done on the binary code. The results of simulating the UEELC method and comparing it with the state-of-the-art methods have shown an improvement of 2.7% of energy consumption of battery.

Summary form only given. We propose a novel compression method, static PPM (SP), which supports partial decode (p-decode) with low memory and computation requirement. P-decode is a function that decodes data from an arbitrary position without decoding the whole, which is critical for exploiting large data in a compressed state. While conventional compression methods do not support p-decode, recent self-indexing data structures, such as compressed suffix arrays (CSA) (Sadakane, K., J. Algorithms, vol.48, no.2, p.294-313, 2003) and FM-index (Ferragina, P. and Manzini, G. Proc. ACM-SIAM SODA, p.269-78, 2001), support p-decode. However, they have to store the whole compressed data in memory, since the order of the data is not preserved after compression. This causes a serious problem when the compressed data is larger than memory size. In contrast, SP does not have to store compressed data in memory and, thus, is the first compression method that supports p-decode for very large compressed data. In order to show SP's high compression performance inherited from PPM and its p-decode performance, we compared SP, CSA, FM-index, gzip (with option -9) and bzip (with option -9) in terms of speed and compression ratio. The results show that SP achieves fast p-decode with high compression ratio.

Time series compression encodes the information in a time-ordered sequence of data points into fewer bits, thereby reducing storage costs and possibly other costs. Compression methods are either general or XOR-based. General compression methods are time-consuming and are not suitable in streaming scenarios, while XOR-based methods are unable to consistently maintain high compression ratios. Further, existing methods compress the integer and decimal parts of floating-point values as a whole, thus disregarding the different characteristics of the two parts. We propose Camel , a new compression method for floating-point time series with the goal of advancing the compression ratios and efficiency achievable. Camel compresses the integer and decimal parts of the double-precision floating-point numbers in time series separately; and instead of performing XOR operations on values using their previous value, Camel identifies values that enable higher compression ratios. Camel also includes means of indexing compressed data, thereby making it possible to query compressed data efficiently. We report on an empirical study of Camel and 11 lossless and 6 lossy compression methods on 22 public datasets and three industrial datasets from AliCloud. The study offers evidence that Camel is capable of outperforming existing methods in terms of both compression ratio and efficiency and is capable of excellent compression performance on both time series and non-time series data.

Ion mobility spectrometry is a rapid scanning measurement method for which compression methods that facilitate the handling of large collections of data are beneficial. Peak distortion in reconstructed ion mobility spectra from linear wavelet compression is problematic in that artifact peaks may cause false positive alarms. Peak shifting also may cause false alarms if target peaks shift out of or interfering peaks shift into detection windows. Nonlinear wavelet compression (NLWC) preserves peak shape and can lessen the degree of distortion, shifting, and artifact peaks in the reconstructed spectra. NLWC was applied to achieve high compression and fidelity in the reconstructed spectra. Another benefit is that NLWC improves signal-to-noise ratios and thus the models built from compressed data are improved. By compressing both the drift time order and the spectrum acquisition order, greater compressions maybe achieved. A two-way nonlinear wavelet compression method that incorporates alternating least squares (2W-NLWC-ALS) algorithm was devised by applying ALS to partially reconstructed wavelet coefficients generated from two-way NLWC. The number of components in a data set can be determined automatically using ASIMPLISMA. The smaller ALS models are saved as the final compressed data and can be used to reconstruct the entire data set efficiently without maintaining the compressed wavelet coefficient matrix of the original data set. The 2W-NLWC-ALS algorithm provides greater compression ratios compared to regular wavelet compression and interpretable models. Using this method, large volumes of data can be acquired and easily evaluated through a simple compressed model. A compression ratio of 510 ppm, root-mean-square error (E(RMS)) of 6.3 mV (full-scale signal is usually 1 V or larger), and relative root-mean-square error (RE(RMS)) of 1.62% were achieved for data sets collected by CAM. A compression ratio of 46 ppm, E(RMS) of 9.2 mV, and RE(RMS) of 0.42% were achieved for data sets collected with an ITEMISER instrument. The 2W-NLWC-ALS algorithm is an efficient compression method that provides the benefits of a simple model.

With the development of the pavement management system (PMS), the amount of the acquired and transmitted date is getting much larger [1][2]. Regard this, a compression method is needed for data analyzing. As there are lots of differences between the image of PMS and general image in the luminance distribution and the useful information region, general compression methods can not fit the special compression requirement of the PMS image. Although the PMS develops rapidly, the relevant image compression has little significant study. Based on Huffman coding and edge detection, this paper provides an efficient compression method, which can reserve all the useful information of the original image. The preliminary results show that the compression ration is higher than IS, which means it can work for the PMS.

Images include information about human body which is used for different purpose such as medical examination security and other plans Compression of images is used in some applications such as profiling information and transmission systems. Regard to importance of images information, lossless or loss compression is preferred. Lossless compressions are JPEG, JPEG-LS and JPEG2000 are few well-known methods for lossless compression. We will use differential pulse code modulation for image compression with Huffman encoder, which is one of the latest and provides good compression ratio, peak signal to noise ratio and minimum mean square error. In real time application which needs hardware implementation, low complex algorithm accelerate compression process. In this paper, we use differential pulse code modulation for image compression lossless and near-lossless compression method is introduced which is efficient due to its high compression ratio and simplicity. This method is consists of a new transformation method called Enhanced DPCM Transformation (EDT) which has a good energy compaction and a suitable Huffman encoding. After introducing this compression method it is applied on different images from Corel dataset for experimental results and analysis. Also we compare it with other existing methods with respect to parameter compression ratio, peak signal noise ratio and mean square error.

In this paper, we extract the facial feature points and their corresponding feature vertices on the face model by hand according to the FDP of MPEG-4, and then improve the traditional radial basis functions (RBF) interpolation algorithm to fit the face model more smoothly and more realistically. In face model texture mapping, a new texture mapping method is proposed, which needs not create full view panoramic image mosaics in advance and avoids sampling them several times. Therefore, it preserves the original information of input face images, which makes the resulting 3D face more realistic and facial details clearer. In addition, when rendering the face model, we partition it into three blocks and make texture mapping for each one with its associated texture, so we render the whole face model only once, which decreases the complexity of computation and huge RAM, and improves face synthesis quality. The experimental results indicate the whole face synthesis process is fast and stable, and the quality of the resulting face is equivalent to the original photographs basically.

The exponentially increasing high throughput of sequencing data calls for efficient specific compression methods to address the challenges posed by the storage and transmission of such data. In this work, we develop a GPU version of lossless reference-based compression method namely G-FQZip by introducing a GPU-based arithmetic coding, a template matching approach, and a parallel light-weight mapping model. The comparison experiments demonstrate that G-FQZip can improve the (de) compression speed while maintaining comparable compression ratios. Besides, the follow-up evaluation demonstrated the efficiency of the GPU-based arithmetic coding and the template matching approach.

This paper conducts a comparative analysis of the impact of unsparsing by data scaling on lossless data compression methods. The most commonly used data compression algorithms such as gzip, zlib, bzip2, and lzma are tested by their compression efficiency in unsparsed datasets by the performance metrics such as compression time, decompression time, and compression ratio for both the original and scaled datasets. Five different data scaling techniques are investigated to transform the original data into unsparsed data such as min-max, robust, absolute max, standardization, and normalization. Our research reveals relationships between data scaling methodologies and compression performance, with disparities in compression efficiency and computational complexity. Fur-thermore, we investigate the effects of scaling on compression ratio and provide results for improving the understanding of the factors that influence lossless data compression methods for non-sparse tabular data.

<p>The projection into 2030 of the climate data volume increase brings an important challenge to the climate science community. This is particularly true for the CMIP7 that is projected to need about an Exabyte of storage capacity. Error-bounded lossy compression is explored as a potential solution to the above problem by different climate research teams. Several lossy compression schemes have been proposed leveraging different forms of decorrelation (transforms, prediction, HoSVD, DNN), quantization (linear, non-linear, vector), and encoding (dictionary-based, variable length, etc.) algorithms. Our experience with different applications shows that the compression methods often need to be customized and optimized to fit the specificities of the datasets to compress and the user requirements on the compression quality, ratio, and throughput. However, none of the existing lossy compression software for scientific data has been designed to be customizable. To address this issue, we developed SZ3, an innovative customizable, modular compression framework. SZ3 is a full C++ refactoring of SZ2 enabling the specialization, addition, or removal of each stage of the lossy compression pipeline to fit the specific characteristics of the datasets to compress and the use-case requirements. This extreme flexibility allows adapting SZ3 to many different use-cases, from ultra-high compression for visualization to ultra-high-speed compression between the CPU (or GPU) and the memory. Thanks to its unique set of features: customization, high compression ratio, high compression throughput, and excellent accuracy preservation, SZ3 won a 2021 R&D100 award. In this presentation, we present SZ3 and a new data prediction-based decorrelation method that significantly improves the compression ratios for climate datasets over the state-of-the-art lossy compressors, while preserving the same data accuracy. Experiments based on CESM datasets show that SZ3 can lead to up to 300% higher compression ratios than SZ2 with the same compression error bound and similar compression throughput.</p>

Bundle block adjustment of large-scale remote sensing data with Block-based Sparse Matrix Compression combined with Preconditioned Conjugate Gradient

Normal maps play an important role in realistic 3D image rendering to express pseudo roughness of the surface with small amount of polygon data. In this paper, a fast and efficient normal map compression algorithm is proposed based on vector quantization and entropy coding. Using the strong correlation among x, y, and z components of normal maps owing to the unity condition, compression ratio has been made much better than conventional approaches. In addition, the encoding time has been made reasonable by considering the distribution of the data and employing inner product in nearest-neighbor search instead of Euclidian distance taking advantage of the unity condition of the training data.

The trajectory data of vessel AIS (automatic identification system) has important theoretical and application value for information supporting decisions. However, large sizes lead to difficulties in storing, querying, and processing. To solve the problems of high compression ratio and longtime consumption of the existing online trajectory compression algorithm, an SPM (scan-pick-move) trajectory data compression algorithm added sliding window is proposed. In order to better compress vessel trajectory data regarding compression efficiency, the sliding window is added to the classical SPM algorithm. In order to reduce trajectory data storage space, the maximum offset distance reference trajectory point is used as the criterion of whether the current trajectory point can be compressed. In this paper, the multi-dimensional space-time characteristics of trajectory data, such as distance error, compression ratio and compression time, are selected to evaluate the trajectory compression method from three levels: geometric characteristics, motion characteristics and compression efficiency. Compared with the existing SPM trajectory data compression algorithm, parallel experiments are conducted based on AIS data gathered over the duration of a month in the Japan Osaka Bay. The SPM trajectory compression algorithm added sliding window can significantly reduce the compression time and outperforms other existing trajectory compression algorithms in term of average compression error at high compression strengths. Also, the proposed method has high compression efficiency in the range of commonly used compression thresholds.